Detecting offensive language online is becoming more and more important [1]}, [2]}, [3]}. To build an effective classifier, one of the major problems is to find the appropriate features. Normally, two types of features are utilized: surface features like \(n\) -grams and word representations trained by neural network. Most offensive language classifiers are trained on different types of surface features with approaches like SVM [3]}, [5]}, Random Forest [6]}, and Logistic Regression [7]}. Recently, word embeddings trained in neural networks have been shown to achieve good performance in offensive language identification tasks [8]}. Benchmarks of the first shared task on aggression identification [9]} show that half of the top 15 systems are trained on neural networks.
